OP_analysis

Loading, Cleaning, and Tidying the Data

Setting path to Excel files as variables

ko_path <- "data/OP_KO_Scotopic.xlsx"
wt_path <- "data/OP_WT_Scotopic.xlsx"

Creating functions for reading sheets into single df and extracting header names

• Here, every sheet in the workbook is given an “id” (its sheet name) before they are concatenated into one large data frame
• The first sheet is ignored since it does not adhere to the format of the rest
• The first column of every sheet is removed since it contains redundant information
• Ideally, I will not hard code the column range so that this would not break if more columns are added

These header functions take header names from different cells in the orginal excel file and concatenate them

These functions are performed separately on wt and ko data

#function for getting header information from continuous cells on the original excel spreadsheet
#from_sheet specifies which sheet to take the headers from in case there is one sheet where the header values are not representative of the rest
xl_header1 <- function(path, header_range, from_sheet) {
    header_names <- read_excel(path = path, range = header_range, sheet = from_sheet)
    names(header_names)
}

#function for dropping sheets from the workbook, setting range, concatenating remaining sheets into single df
xl_concat <- function(path, sheet_rm, range, col_names) {
  path %>% 
    excel_sheets() %>% 
    list.remove(sheet_rm) %>% 
    purrr::set_names() %>% 
    map_df(~ read_excel(path = path, sheet = .x, range = range, col_names = col_names), .id = "sheet")
}

mk_header <- function(xl_path, mouse_range, stat_range, data_range, rm_sheets) {
  
  #gather header names for mice and statistics
  mouse_names <- xl_header1(xl_path, mouse_range, 3)
  stat_names <- xl_header1(xl_path, stat_range, 3)
  final_header <- append(stat_names, mouse_names)
  
  
  #if last element of final header doesnt end in NA, duplicate last element
  #replace any mouse names that begin with "..." with NA
  
  final_header <- final_header %>% 
    str_replace("^\\.+", NA_character_)
  
  last_element <- tail(final_header,1)
  
  if(!is.na(last_element)){
    final_header <- append(final_header,last_element)
  }
  else {
    final_header <- final_header
  }
  
  #replace all NA values with the mouse name that preceeds it. Allows duplication for left and right eyes
  final_header <- final_header %>%
    tibble() %>%
    mutate(zoo::na.locf0(final_header)) %>%
    pull()
  
  xl_concat(xl_path, rm_sheets, data_range, final_header)
}


#building preliminary df
ko_sheets <- mk_header(ko_path,"E1:U1", "B2:D2", "B2:V1822", rm_sheets = c(1,2))

## New names:
## * `` -> ...2
## * `` -> ...4
## * `` -> ...6
## * `` -> ...8
## * `` -> ...10
## * ...

## New names:
## * `051` -> `051...4`
## * `051` -> `051...5`
## * `049` -> `049...6`
## * `049` -> `049...7`
## * `061` -> `061...8`
## * ...
## New names:
## * `051` -> `051...4`
## * `051` -> `051...5`
## * `049` -> `049...6`
## * `049` -> `049...7`
## * `061` -> `061...8`
## * ...
## New names:
## * `051` -> `051...4`
## * `051` -> `051...5`
## * `049` -> `049...6`
## * `049` -> `049...7`
## * `061` -> `061...8`
## * ...
## New names:
## * `051` -> `051...4`
## * `051` -> `051...5`
## * `049` -> `049...6`
## * `049` -> `049...7`
## * `061` -> `061...8`
## * ...
## New names:
## * `051` -> `051...4`
## * `051` -> `051...5`
## * `049` -> `049...6`
## * `049` -> `049...7`
## * `061` -> `061...8`
## * ...
## New names:
## * `051` -> `051...4`
## * `051` -> `051...5`
## * `049` -> `049...6`
## * `049` -> `049...7`
## * `061` -> `061...8`
## * ...
## New names:
## * `051` -> `051...4`
## * `051` -> `051...5`
## * `049` -> `049...6`
## * `049` -> `049...7`
## * `061` -> `061...8`
## * ...
## New names:
## * `051` -> `051...4`
## * `051` -> `051...5`
## * `049` -> `049...6`
## * `049` -> `049...7`
## * `061` -> `061...8`
## * ...
## New names:
## * `051` -> `051...4`
## * `051` -> `051...5`
## * `049` -> `049...6`
## * `049` -> `049...7`
## * `061` -> `061...8`
## * ...
## New names:
## * `051` -> `051...4`
## * `051` -> `051...5`
## * `049` -> `049...6`
## * `049` -> `049...7`
## * `061` -> `061...8`
## * ...
## New names:
## * `051` -> `051...4`
## * `051` -> `051...5`
## * `049` -> `049...6`
## * `049` -> `049...7`
## * `061` -> `061...8`
## * ...
## New names:
## * `051` -> `051...4`
## * `051` -> `051...5`
## * `049` -> `049...6`
## * `049` -> `049...7`
## * `061` -> `061...8`
## * ...
## New names:
## * `051` -> `051...4`
## * `051` -> `051...5`
## * `049` -> `049...6`
## * `049` -> `049...7`
## * `061` -> `061...8`
## * ...

wt_sheets <- mk_header(wt_path, "E1:R1", "B2:D2", "B2:R1822", rm_sheets = c(1,2))

## New names:
## * `` -> ...2
## * `` -> ...4
## * `` -> ...6
## * `` -> ...8
## * `` -> ...10
## * ...
## New names:
## * `051` -> `051...4`
## * `051` -> `051...5`
## * `049` -> `049...6`
## * `049` -> `049...7`
## * `061` -> `061...8`
## * ...
## New names:
## * `051` -> `051...4`
## * `051` -> `051...5`
## * `049` -> `049...6`
## * `049` -> `049...7`
## * `061` -> `061...8`
## * ...
## New names:
## * `051` -> `051...4`
## * `051` -> `051...5`
## * `049` -> `049...6`
## * `049` -> `049...7`
## * `061` -> `061...8`
## * ...
## New names:
## * `051` -> `051...4`
## * `051` -> `051...5`
## * `049` -> `049...6`
## * `049` -> `049...7`
## * `061` -> `061...8`
## * ...
## New names:
## * `051` -> `051...4`
## * `051` -> `051...5`
## * `049` -> `049...6`
## * `049` -> `049...7`
## * `061` -> `061...8`
## * ...
## New names:
## * `051` -> `051...4`
## * `051` -> `051...5`
## * `049` -> `049...6`
## * `049` -> `049...7`
## * `061` -> `061...8`
## * ...
## New names:
## * `051` -> `051...4`
## * `051` -> `051...5`
## * `049` -> `049...6`
## * `049` -> `049...7`
## * `061` -> `061...8`
## * ...
## New names:
## * `051` -> `051...4`
## * `051` -> `051...5`
## * `049` -> `049...6`
## * `049` -> `049...7`
## * `061` -> `061...8`
## * ...
## New names:
## * `051` -> `051...4`
## * `051` -> `051...5`
## * `049` -> `049...6`
## * `049` -> `049...7`
## * `061` -> `061...8`
## * ...
## New names:
## * `051` -> `051...4`
## * `051` -> `051...5`
## * `049` -> `049...6`
## * `049` -> `049...7`
## * `061` -> `061...8`
## * ...
## New names:
## * `051` -> `051...4`
## * `051` -> `051...5`
## * `049` -> `049...6`
## * `049` -> `049...7`
## * `061` -> `061...8`
## * ...
## New names:
## * `051` -> `051...4`
## * `051` -> `051...5`
## * `049` -> `049...6`
## * `049` -> `049...7`
## * `061` -> `061...8`
## * ...
## New names:
## * `051` -> `051...4`
## * `051` -> `051...5`
## * `049` -> `049...6`
## * `049` -> `049...7`
## * `061` -> `061...8`
## * ...

Handling multi-header layout and building the data frame

• When imported into a data frame, this data set has 2 headers
  • Header 1: Mouse code names (i.e. 051, 049, 061…)
  • Header 2: Time, Mean, SEM, and mouse eyes (Left vs Right)

This code combines the two headers of the data set into one while merging “Left” and “Right” names with the appropriate mouse

ko_final_header <- names(ko_sheets) %>% 
  str_replace("\\..*", "")
ko_final_header <- ko_final_header[-1]

row1 <-as.list(ko_sheets[1,])
row1 <- row1[-1]

ncols <- length(row1)

for (n in 1:ncols){
  if(row1[n] == "Left" | row1[n] == "Right"){
    as.character(ko_final_header, row1)
    ko_final_header[n] <- paste0(ko_final_header[n],sep = "_", row1[n])
  }
}

#building final data frame
ko_sheets <- xl_concat(ko_path, c(1,2),"B2:V1822", ko_final_header)
head(ko_sheets)

Tidying the Data

First, we have to eliminate all rows in the df that are filled with extra header names

• For every sheet imported, there is a row with the values “time”, “mean”, “SEM”. These rows can be removed

#removing extra header rows
ko_sheets <- ko_sheets %>% 
  filter(!Time == "Time")

Now we must gather the data to make variables for eye and mouse

#index the first column to which we want to apply pivot_longer (the first col with mouse name in it)
headers <- as.list(names(ko_sheets))
ncols <- ncol(ko_sheets)
start_col <- which(str_detect(headers, "_")) %>% 
  first()

ko_sheets <- ko_sheets %>% 
  pivot_longer(cols = start_col:ncols, names_to = c("mouse", "eye"), values_to = "response", names_sep = "_") %>% 
  mutate(response = as.double(response),
         SEM = as.double(SEM),
         Mean = as.double(Mean),
         Time = as.integer(Time),
         eye = as.factor(eye),
         mouse = as.factor(mouse)
         )

## Note: Using an external vector in selections is ambiguous.
## ℹ Use `all_of(start_col)` instead of `start_col` to silence this message.
## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.

## Note: Using an external vector in selections is ambiguous.
## ℹ Use `all_of(ncols)` instead of `ncols` to silence this message.
## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.

ko_sheets

Building Function for Finding Local Maxima and Minima

Employing the stat_peak function to detect local extrema. Excludes peaks and valleys that are within 10 peaks of the highest/lowest

• The mouse_grapher function plots the local extrema on the OP data

  • The arguments span and threshold control the thresholding parameters of the extrema detection

mouse_grapher <- function(df, intensity, ms_code, time_window = 500, span = 7, threshold = 0.65, result = "both") {
  
  #plot the data and show local extrema
   mouse_plot <- df %>% 
    filter(mouse == ms_code , sheet == intensity , Time <= time_window, !is.na(response)) %>% 
    ggplot(aes(x = Time, y = response)) +
    geom_line(aes(color = eye), alpha = 0.7) +
    suppressWarnings(stat_peaks(aes(shape = eye, color = eye),span = span, geom = "point", ignore_threshold = threshold, na.rm = TRUE))+
    suppressWarnings(stat_peaks(aes(color = eye, shape = eye),
               na.rm = TRUE,
               geom = "text", hjust = -0.2, vjust = 0.5, 
               angle = 90, check_overlap = TRUE, response.label.fmt = "i",
               span = span, ignore_threshold = threshold,
               alpha = 0.7))+
    facet_wrap(~eye, nrow = 2, ncol = 1, scales = "fixed") +
    ggtitle(ms_code)+ 
    theme_bw() 
  
  #collect the extrema values in a table   
  OP_max <- layer_data(mouse_plot, i = 2L) %>% 
    select(x, y, shape) %>% 
    mutate(eye = case_when(
      shape == 16 ~ "Left",
      shape == 17 ~ "Right"
    )) %>% 
    select(-shape) %>%
    kable(
      caption = paste0("Mouse:", sep = " ", ms_code, "|", sep = " ", "Intensity: ", intensity),
      align = "c") %>%
    row_spec(0, bold = TRUE) %>%
    kable_styling(bootstrap_options = c("striped", "condensed"),
                  full_width = T,
                  fixed_thead = T)

  if(result == "plot") {
  print(mouse_plot)
  }
  if(result == "table"){
  print(OP_max)
  }
  if(result == "both"){
    print(mouse_plot)
    OP_max
  }
}

Implementing mouse_grapher for each mouse and intensity

Loop that iterates through all Sheets and Mice

#These have been modified for loop testing purposes
sheets <- as.character(unique(ko_sheets$sheet))
mice <- as.character(unique(ko_sheets$mouse))
mice <- mice[!(mice %in% c("141", "113", "138"))]


#for loop attempt
for (i in sheets) {
  cat("  \n##", i, "{.tabset}", "  \n")
  for (j in mice) {
    cat("  \n###", "**Mouse: **", j, "  \n")
    mouse_grapher(ko_sheets, intensity = i , ms_code = j , span = 9, threshold = 0.62, result = "plot")
    cat("\n")
    as.data.frame(mouse_grapher(ko_sheets, intensity = i , ms_code = j , span = 9, threshold = 0.62, result = "table"))
  }
}

-4.14

Mouse: 051

Mouse: 051| Intensity: -4.14

x	y	eye
-8	1.24090	Left
11	1.18818	Left
45	1.09540	Left
95	1.21664	Left
105	1.42383	Left
112	1.15605	Left
122	2.53817	Left
134	2.71242	Left
145	2.90210	Left
148	4.11104	Left
161	3.23271	Left
165	1.58283	Left
175	1.88722	Left
178	1.13469	Left
189	1.93200	Left
192	1.15978	Left
201	1.47764	Left
205	2.22281	Left
222	1.53311	Left
239	1.44728	Left
278	1.31225	Left
295	1.27285	Left
312	1.14982	Left
322	1.10552	Left
335	1.29737	Left
345	1.11577	Left
372	1.34661	Left
378	1.12722	Left
462	1.07657	Left
88	0.96296	Right
97	1.13759	Right
108	1.54760	Right
121	1.82385	Right
133	2.42247	Right
147	1.77486	Right
161	0.95166	Right
174	0.81856	Right
201	0.73861	Right
261	1.34024	Right
286	0.83588	Right
345	0.88132	Right
422	0.95323	Right
475	0.86831	Right

Mouse: 049

Mouse: 049| Intensity: -4.14

x	y	eye
-2	0.74412	Left
6	0.86474	Left
19	0.89774	Left
25	0.61109	Left
32	0.79786	Left
42	0.55217	Left
52	0.54301	Left
58	0.43984	Left
69	0.87290	Left
85	0.76270	Left
102	1.41235	Left
113	0.52472	Left
126	1.54179	Left
141	0.70765	Left
152	1.13606	Left
175	0.65776	Left
185	0.77410	Left
196	0.64786	Left
219	0.50028	Left
229	0.45905	Left
236	0.64882	Left
253	0.60360	Left
269	0.50844	Left
279	1.12072	Left
289	0.83260	Left
299	0.57214	Left
302	0.64679	Left
313	0.81798	Left
331	0.43883	Left
341	0.93853	Left
352	0.84510	Left
365	0.74840	Left
375	1.23917	Left
385	0.78759	Left
396	0.49557	Left
402	0.43372	Left
415	0.44905	Left
436	0.45076	Left
453	0.95022	Left
462	0.62140	Left
479	0.56006	Left
34	0.32245	Right
65	0.31771	Right
86	0.51817	Right
96	0.80255	Right
107	1.02188	Right
121	1.12354	Right
133	0.73578	Right
148	0.50421	Right
160	0.33542	Right
206	0.32094	Right
222	0.36449	Right
272	0.33867	Right
370	0.38356	Right
379	0.36913	Right

Mouse: 061

Mouse: 061| Intensity: -4.14

x	y	eye
8	1.03097	Left
13	0.93419	Left
17	0.87888	Left
24	1.09941	Left
46	1.18018	Left
62	1.21492	Left
74	1.12476	Left
79	0.89640	Left
90	1.08791	Left
112	2.47438	Left
124	1.66278	Left
136	1.26088	Left
139	0.96891	Left
151	1.71050	Left
163	1.44973	Left
214	1.06463	Left
224	0.92220	Left
229	1.10075	Left
247	1.17762	Left
253	0.88933	Left
257	1.20506	Left
267	1.11444	Left
274	1.03156	Left
280	0.96994	Left
291	0.98368	Left
302	1.10301	Left
323	0.96425	Left
329	0.88898	Left
339	1.05197	Left
367	0.86734	Left
389	0.92659	Left
407	1.38957	Left
417	1.00999	Left
467	1.55637	Left
474	1.15885	Left
479	1.16137	Left
484	0.90845	Left
491	1.27007	Left
-8	0.76590	Right
-4	0.29981	Right
-1	0.45236	Right
4	0.70816	Right
9	0.62923	Right
16	0.71168	Right
22	1.16042	Right
24	0.37848	Right
27	0.40714	Right
32	0.70962	Right
37	0.53347	Right
42	0.58180	Right
48	0.30149	Right
55	0.41622	Right
61	0.87081	Right
65	0.88567	Right
70	0.58759	Right
77	1.21053	Right
88	0.50360	Right
92	1.05300	Right
101	0.31490	Right
105	1.23521	Right
115	0.98927	Right
128	0.57515	Right
132	0.78453	Right
135	0.46464	Right
138	0.35481	Right
143	0.62343	Right
147	0.31017	Right
154	0.69778	Right
160	0.47550	Right
163	0.32018	Right
167	0.64692	Right
171	0.57777	Right
175	0.96699	Right
182	0.75450	Right
187	0.72519	Right
193	0.63822	Right
198	0.62708	Right
204	0.75382	Right
209	0.33751	Right
216	0.79583	Right
221	0.70907	Right
225	0.59106	Right
232	0.76124	Right
238	0.96769	Right
244	0.50416	Right
250	0.47157	Right
254	0.71585	Right
258	0.33815	Right
261	0.83424	Right
266	0.88818	Right
270	0.60097	Right
274	0.43247	Right
278	0.57954	Right
281	0.64146	Right
287	0.91494	Right
292	0.47473	Right
297	0.42532	Right
300	0.41185	Right
304	0.72363	Right
309	0.83114	Right
311	0.60436	Right
315	0.41458	Right
321	0.52767	Right
326	0.42972	Right
333	0.78166	Right
338	0.86570	Right
342	0.37435	Right
351	0.76466	Right
360	0.47482	Right
365	0.47704	Right
372	0.59751	Right
375	0.40078	Right
378	0.70090	Right
381	0.73080	Right
388	0.74874	Right
393	0.76130	Right
399	1.10924	Right
404	0.68595	Right
408	0.30630	Right
411	0.43917	Right
415	0.96848	Right
420	0.92951	Right
426	0.41020	Right
432	0.99094	Right
438	0.49815	Right
443	0.54910	Right
449	0.36355	Right
454	0.61173	Right
459	0.40194	Right
466	0.47342	Right
470	0.91107	Right
475	0.81651	Right
482	0.55421	Right
488	1.03854	Right
493	0.52210	Right

Mouse: 074

Mouse: 074| Intensity: -4.14

x	y	eye
-6	3.10710	Left
0	2.15748	Left
3	1.62493	Left
7	1.58400	Left
10	3.38331	Left
17	1.94278	Left
21	1.48043	Left
24	2.47035	Left
27	3.05948	Left
30	1.70154	Left
36	2.02829	Left
40	2.82826	Left
43	3.00203	Left
47	1.45356	Left
54	1.65256	Left
57	2.15815	Left
67	1.73679	Left
70	2.74765	Left
73	2.34227	Left
77	2.31523	Left
84	2.27592	Left
86	2.94059	Left
90	2.19774	Left
93	1.48113	Left
100	2.05773	Left
107	4.53820	Left
110	4.83212	Left
120	3.61080	Left
123	1.76257	Left
127	1.60165	Left
130	2.95525	Left
133	1.87961	Left
140	2.20037	Left
143	4.71480	Left
147	1.27036	Left
150	1.29659	Left
153	1.82112	Left
157	2.70707	Left
160	2.72199	Left
167	2.21317	Left
170	1.71209	Left
173	1.89101	Left
177	2.97054	Left
183	2.06602	Left
186	2.14409	Left
190	2.75237	Left
194	3.01050	Left
200	1.75438	Left
204	2.80612	Left
207	1.35750	Left
210	2.26663	Left
213	1.52755	Left
217	2.24405	Left
220	2.06290	Left
223	2.79209	Left
227	2.44465	Left
233	1.28440	Left
236	1.45042	Left
240	2.25231	Left
243	2.81828	Left
250	2.51107	Left
257	1.41573	Left
260	2.09887	Left
263	1.96320	Left
267	2.34180	Left
273	2.35012	Left
277	2.94408	Left
286	2.53264	Left
290	1.75877	Left
293	4.10340	Left
304	2.16447	Left
307	2.25066	Left
310	2.19174	Left
321	1.54812	Left
323	2.15515	Left
327	2.06650	Left
330	1.79962	Left
333	1.79389	Left
340	1.42467	Left
343	2.74972	Left
350	1.74186	Left
357	2.41331	Left
360	1.98787	Left
367	2.78668	Left
370	1.78399	Left
373	2.28479	Left
377	3.46380	Left
383	1.53903	Left
386	2.05566	Left
390	2.00189	Left
393	2.33015	Left
400	1.86003	Left
403	1.31054	Left
407	2.44784	Left
410	2.80612	Left
413	1.87654	Left
416	1.61530	Left
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Mouse: 051| Intensity: 0.6

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Mouse: 051| Intensity: 2.22

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Mouse: 061| Intensity: 2.22

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p0.6

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Mouse: 051| Intensity: p0.6

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Mouse: 049| Intensity: p0.6

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Mouse: 061| Intensity: p0.6

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Mouse: 074| Intensity: p0.6

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Mouse: 078| Intensity: p0.6

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Mouse: 083| Intensity: p0.6

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p1.19

Mouse: 051

Mouse: 051| Intensity: p1.19

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Mouse: 049| Intensity: p1.19

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Mouse: 061

Mouse: 061| Intensity: p1.19

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Mouse: 074| Intensity: p1.19

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Mouse: 078| Intensity: p1.19

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Mouse: 083| Intensity: p1.19

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p1.91

Mouse: 051

Mouse: 051| Intensity: p1.91

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Mouse: 049

Mouse: 049| Intensity: p1.91

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Mouse: 061| Intensity: p1.91

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Mouse: 074| Intensity: p1.91

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Mouse: 078| Intensity: p1.91

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Mouse: 083| Intensity: p1.91

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p2.22

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Mouse: 051| Intensity: p2.22

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Mouse: 049| Intensity: p2.22

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Mouse: 061| Intensity: p2.22

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Mouse: 074| Intensity: p2.22

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Mouse: 078| Intensity: p2.22

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Mouse: 083| Intensity: p2.22

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128	3.63203	Left
134	5.33241	Left
137	4.84889	Left
140	5.92329	Left
149	2.54982	Left
173	2.08875	Left
176	1.47728	Left
182	4.69923	Left
188	3.12109	Left
191	4.40908	Left
194	5.58984	Left
200	2.13040	Left
228	2.92400	Left
234	2.50614	Left
237	2.20432	Left
240	3.61337	Left
249	3.90754	Left
255	1.46326	Left
261	2.20697	Left
282	1.34916	Left
288	1.26935	Left
294	4.11916	Left
300	2.08527	Left
303	1.34446	Left
306	2.32461	Left
313	1.11215	Left
315	1.78581	Left
328	1.54841	Left
334	0.85210	Left
340	2.45943	Left
348	2.58947	Left
355	1.67561	Left
361	3.29263	Left
368	1.39808	Left
373	1.77279	Left
382	1.75097	Left
388	0.88945	Left
394	2.49438	Left
401	1.44410	Left
403	1.18983	Left
407	1.76114	Left
413	1.59947	Left
416	1.75153	Left
421	0.92096	Left
428	3.04502	Left
434	1.70343	Left
437	1.18418	Left
440	1.79317	Left
449	1.70721	Left
455	1.20660	Left
458	1.27048	Left
461	2.39014	Left
470	0.90133	Left
473	1.68115	Left
482	2.35037	Left
488	1.46485	Left
494	2.50417	Left
79	5.18426	Right
83	5.35685	Right
88	5.69860	Right
134	10.40420	Right
183	7.24347	Right
236	4.98981	Right
285	2.62280	Right
288	3.06658	Right
293	2.72360	Right

mouse_grapher(ko_sheets, intensity = -2.12 , ms_code = "051" , span = 9, threshold = 0.65, result = "table")

Mouse: 051| Intensity: -2.12

x	y	eye
46	8.40923	Left
57	19.64693	Left
67	28.06960	Left
76	28.06506	Left
89	15.47989	Left
101	9.00723	Left
54	13.26365	Right
63	19.67042	Right
73	25.08919	Right
83	17.32882	Right
95	7.84280	Right